Prospect de-risking using CSEM
Joanne Suffert*, EMGS, Volker Ullrich, EMGS, Marit Tørudbakken Jendal, RWE DEA Norge
Summary
Controlled source electromagnetic (CSEM) data have been
acquired along three 2D lines that cover two prospects
offshore Norway. The purpose of the survey was to assess
the hydrocarbon potential of these prospects as part of the
exploration risking workflow
A cross-disciplinary interpretation of the CSEM data,
including seismic interpretation, 3D inversion and postsurvey 3D modeling, gave rise to a final geo-resistivity
model that explains the data in a satisfactory way. Based on
this interpretation, it was concluded that the prospects are
likely to have either low hydrocarbon saturation or be water
filled, and they were downgraded accordingly. A deep sill
intrusion (2 km burial depth), crossed by one of the
acquisition lines and expected to be highly resistive, was
used as a calibration target. The sill intrusion was clearly
detected and imaged by the 3D inversion, thus increasing
our confidence in the data analysis.
The concept of CSEM is based on the fact that the
attenuation and velocity of electromagnetic energy is
determined by formation resistivity and the source
frequency. Hydrocarbon saturated sediments generally have
higher resistivities than brine saturated sediments due to the
fluid properties. However, subsurface resistivity anomalies
are not restricted to hydrocarbon saturated sediments.
Lithologies such as tight (cemented) sediments, limestone,
salt and magmatic intrusions are known to have potentially
high resistivities.
Survey layout and electromagnetic attribute analysis
Introduction
Controlled source electromagnetics (CSEM) is a remote
sensing technique, which provides information about
subsurface resistivity variations using electromagnetic
energy. The method was demonstrated both theoretically
(Kong et al., 2002) and in practice by several calibration
and commercial surveys (Ellingsrud et al., 2002).
Figure 1: Survey layout. Black squares represent receiver
positions. Prospect outlines are shown in red for prospect 1 (1
km burial depth) and blue for prospect 2 (2 km burial depth). The
black outline marks the sill intrusion.
Figure 2: Normalized magnitude map at 3000m offset (top) and
6000m offset (bottom) at 0.5Hz. Prospect outlines are shown in
pink for prospect 1 and red for prospect 2. The black outline
marks the sill intrusion.
Prospect de-risking using CSEM
A total of 58 receivers were deployed along three receiver
lines (Figure 1). The water depth in the area ranges from
500 m to 1100 m.
The source pulse was designed to focus the energy on three
frequencies, 0.25Hz, 0.5Hz and 1.0Hz (Mittet et al., 2007),
in order to be able to image both shallow and deep targets,
using high and low frequencies, respectively.
The dense receiver spacing (1.25 km along Tx001 and
Tx002 and 1.5 km along Tx003) is very suitable for
advanced processing such as 3D inversion to be performed.
Azimuth data has been acquired at the line crossings,
increasing the data coverage (Maaø et al., 2007, Thrane et
al., 2007).
After standard processing, all data have been compared to a
single reference receiver located in the southern part of line
Tx001. This process is called normalization and provides a
means of comparing responses recorded at different
receiver locations. By calculating the normalized response
for each receiver at a given source-receiver offset, resistive
and conductive anomalies are identified along the survey
line. The normalized values can be plotted in map view to
locate the anomaly spatially. Such maps are shown in
Figure 2, with relatively short offsets (3000 m) representing
shallow regions of the subsurface and relatively long
offsets (6000 m) describing deeper regions of the
subsurface.
anomaly and one outside is presented in Figure 3. The
phase of the receiver located above this anomaly starts to
decrease from 5 km offset, indicating a highly resistive
layer at depth. Combined with the seismic interpretation,
this anomaly corresponds spatially with the outline of a sill
intrusion (black outline in Figure 2).
3D inversion and 3D modeling results
3D modeling and 3D inversion were used to assess the
resistivity distribution in the subsurface and to find the
most probable resistivity values for the prospects.
Pre-survey sensitivity modeling, based on horizons from
seismic interpretations and resistivity values from nearby
wells, was performed to assess the response of potential
reservoirs. In post-survey modeling, the main aim is to
create the best possible background model and compare it's
response with the real data. Commonly, the model
Several resistive anomalies are visible. Most of these
anomalies appear at relatively short offsets, indicating
shallow resistivity variation. Towards the southern part of
line Tx003, one anomaly appears at longer offsets. A
comparison between a receiver located on top of this
Figure 3: Magnitude and phase versus offset, comparing the
reference receiver (orange) to a receiver located above the sill
intrusion (blue).
Figure 4: 3D background model (top) and 3D reservoir model
(bottom). Color scale shows the vertical resistivities.
Prospect de-risking using CSEM
geometry remains similar to the pre-survey study but the
background resistivities are replaced by values derived
from 1D inversion (Roth et al., 2007) or 1D modeling of
several real receivers. The modeled resistivities show a
clear indication of anisotropy (factor 2 to 4) in the shallow
layers of the subsurface.
Direct comparison of real and modeled data are shown in
Figure 6. The mismatch is calculated with the following
formula, where E represents the electric field:
By introduction or removal of resistors, each prospect or
any combination of them can be modeled and compared to
the measured data (Figure 4 and Figure 6). We found that
several of the measured resistive anomalies can be
explained by our background model.
In parallel with 3D post-survey modeling, 3D inversion
was performed along the three lines. The inversion of
CSEM data aims at finding a resistivity model of the subsurface that reproduces a set of observed data to within
survey measurement accuracy.
Given the limited data coverage and measurement
uncertainty of the survey, there are several resistivity
models that explain the observed data. To obtain a more
reliable resistivity model from this dataset, inversion was
conducted using different start models and with varying
frequency content. The results from these different
inversions were then subjected to a number of consistency
tests, and were then interpreted using any additional
knowledge of the geology of the survey area.
The results shown here were obtained by inverting the three
main frequencies (0.25Hz, 0.5Hz and 1.0Hz) and with a
half-space start model of constant resistivity.
The data from the inversion results (Figure 5), show
consistent results with the pre/post-survey 3D modeling.
From the cross-sections, there is a relatively high resistivity
in the upper part of the subsurface, followed by a lower
resistivity interval. The resistivity then increases with
depth. The upper part of the subsurface is expected to be
anisotropic. Since the inline source-receiver configuration
is primarily sensitive to vertical resistivity, we expect the
resistivity distribution obtained by the 3D inversion to be
more representative of the vertical resistivity distribution.
On line Tx003 a highly resistive anomaly is visible, starting
approximately 2 km below mud-line. This anomaly
accurately matches the depth of the interpreted sill
intrusion.
Figure 5: 3D inversion results along the three lines (top) and
cross-section along line Tx003 (bottom). Black squares represent
receiver positions. Red indicates areas of high resistivity while
blue indicates areas of lower resistivity.
From the 3D inversion results, no anomalies related to the
prospects are observed. Therefore a sensitivity study is
essential in order to understand why we do not observe any
resistive anomalies and which resistivities the prospects are
likely to have.
Interpretation and integration
The resistive anomaly on line Tx002 (Figure 2), covering
the eastern edge of prospect 2 and extending to the end of
the towline, shows an increasing normalized magnitude
response. Seismic horizons indicate an eastward thickening
of the uppermost sediment layer (Figure 6, top). From 1D
modeling and inversion we know that this layer is most
probably anisotropic. When running an anisotropic 3D
model with a horizontal resistivity of 2 Ωm and vertical
resistivity of 4 Ωm in the top layer, the response from the
pure background model is very similar to the measured data
(Figure 6). Since the EM method is mainly sensitive to the
vertical resistivity
component and that the vertical
resistivity is relatively high, the increase in thickness of the
top layer explains the measured anomaly without
Prospect de-risking using CSEM
introducing any additional resistors. All other shallow
anomalies within the survey area can be linked to this
phenomenon.
Initially, 30 Ωm was the anticipated resistivity value in the
two prospects. However, post-survey modeling showed that
this was too high and only values below 10 Ωm fit the
measured data (Figure 6). Prospects with 30 Ωm show a
large misfit (blue areas) while the 10 Ωm case is not
distinguishable from the pure background model.
This indicates that the prospects are either hydrocarbons
with low saturation values or water filled sediments.
Discussion
Advanced imaging and 3D modeling were used in the postsurvey workflow, leading to a satisfactory interpretation of
the dataset.
The volcanic intrusion was detected in a spatially accurate
position and served as a calibration for the area. All
shallow anomalies could be explained by a pure
background model only including anisotropy and thickness
changes in the uppermost layer of the sedimentary column.
Even though this survey resulted in down-grading the
prospects, it was rated as a success by the client since it
freed resources for other projects and saved further
exploration costs in the area.
Acknowledgements
We would like to thank RWE-DEA Norge for granting
permission to publish the results of this survey. We would
also like to thank all geophysicists and geologists at RWEDEA Norge and EMGS that have contributed with their
expertise and knowledge to the successful processing and
interpretation of the data.
Figure 6: Cross-section along line 2 of the 3D resistivity model
(top). Misfit plots between real and modeled data with
background model - waterfilled sediment (top), with 10 Ωm
prospects (middle) and 30 Ωm prospects (bottom).